Kim JK, Chang MC, Park WT, Lee GW. Identification of L5 vertebra on lumbar spine radiographs using deep learning.
J Int Med Res 2024;
52:3000605231223881. [PMID:
38206194 PMCID:
PMC10785730 DOI:
10.1177/03000605231223881]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 12/14/2023] [Indexed: 01/12/2024] Open
Abstract
OBJECTIVE
Deep learning is an advanced machine-learning approach that is used in several medical fields. Here, we developed a deep learning model using an object detection algorithm to identify the L5 vertebra on anteroposterior lumbar spine radiographs, and assessed its detection accuracy.
METHODS
We retrospectively recruited 150 participants for whom both anteroposterior whole-spine and lumbar spine radiographs were available. The anteroposterior lumbar spine radiographs of these patients were used as the input data. Of the 150 images, 105 (70%) were randomly selected as the training set, and the remaining 45 (30%) were assigned to the validation set. YOLOv5x, of the YOLOv5 family model, was used to detect the L5 vertebra area.
RESULTS
The mean average precisions 0.5 and 0.75 of the trained L5 detection model were 99.2% and 96.9%, respectively. The model's precision was 95.7% and its recall was 97.8%. Furthermore, 93.3% of the validation data were correctly detected.
CONCLUSION
Our deep learning model showed an outstanding ability to identify L5 vertebrae.
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